This project uses PyTorch to classify bone fractures. As well as fine-tuning some famous CNN architectures (like VGG 19, MobileNetV3, RegNet,...), we designed our own architecture. Additionally, we used Transformer architectures (such as Vision Transformer and Swin Transformer). The dataset is Bone Fracture Multi-Region X-ray, available on Kaggle.
The dataset includes fractured and non-fractured X-ray images covering all anatomical body regions, including the lower limb, upper limb, lumbar region, hips, knees, etc. The dataset is divided into three folders, each containing fractured and non-fractured radiographic images. The dataset can be accessed at https://www.kaggle.com/datasets/bmadushanirodrigo/fracture-multi-region-x-ray-data/data.
This dataset contains 10,580 radiographic images (X-ray) data.
Training Data
Number of Images: 9246
Validation Data
Number of Images: 828
Test Data
Number of Images: 506
In machine learning applications involving image data, it is essential to employ a robust preprocessing routine to enhance model training and ensure consistent evaluation. The preprocessing steps typically include resizing all images to a fixed dimension, introducing random transformations such as flips to augment the training dataset, and normalizing the pixel values across images. These random transformations help the model learn to generalize from varied data presentations, effectively improving its robustness. For validation and testing, the preprocessing simplifies; images are resized and normalized in the same way as the training set but without random transformations. This consistency ensures that the model's performance evaluation is based on processing conditions identical to those it trained under, providing a true test of its capabilities on new, unaltered images.
Below is the summary of the CNN model architecture used in our project:
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Layer (type) Output Shape Param #
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Conv2d-1 [-1, 128, 226, 226] 3,584
BatchNorm2d-2 [-1, 128, 75, 75] 256
Conv2d-3 [-1, 256, 77, 77] 295,168
BatchNorm2d-4 [-1, 256, 25, 25] 512
Conv2d-5 [-1, 256, 27, 27] 590,080
BatchNorm2d-6 [-1, 256, 9, 9] 512
Linear-7 [-1, 256] 5,308,672
Linear-8 [-1, 256] 65,792
Linear-9 [-1, 256] 65,792
Linear-10 [-1, 256] 65,792
Linear-11 [-1, 1] 257
================================================================
Total params: 6,396,417
Trainable params: 6,396,417
Non-trainable params: 0
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Input size (MB): 0.57
Forward/backward pass size (MB): 69.76
Params size (MB): 24.40
Estimated Total Size (MB): 94.74
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precision recall f1-score support
fractured 0.93 0.96 0.94 238
not fractured 0.96 0.93 0.95 268
accuracy 0.94 506
macro avg 0.94 0.95 0.94 506
weighted avg 0.95 0.94 0.94 506
precision recall f1-score support
fractured 0.87 0.90 0.88 238
not fractured 0.91 0.88 0.89 268
accuracy 0.89 506
macro avg 0.89 0.89 0.89 506
weighted avg 0.89 0.89 0.89 506
precision recall f1-score support
fractured 0.73 0.95 0.82 238
not fractured 0.93 0.69 0.79 268
accuracy 0.81 506
macro avg 0.83 0.82 0.81 506
weighted avg 0.84 0.81 0.81 506
precision recall f1-score support
fractured 0.81 0.76 0.79 238
not fractured 0.80 0.84 0.82 268
accuracy 0.81 506
macro avg 0.81 0.80 0.80 506
weighted avg 0.81 0.81 0.81 506
precision recall f1-score support
fractured 0.86 0.89 0.88 238
not fractured 0.90 0.87 0.89 268
accuracy 0.88 506
macro avg 0.88 0.88 0.88 506
weighted avg 0.88 0.88 0.88 506
precision recall f1-score support
fractured 0.95 0.95 0.95 238
not fractured 0.96 0.95 0.95 268
accuracy 0.95 506
macro avg 0.95 0.95 0.95 506
weighted avg 0.95 0.95 0.95 506
precision recall f1-score support
fractured 0.88 0.89 0.88 238
not fractured 0.90 0.89 0.89 268
accuracy 0.89 506
macro avg 0.89 0.89 0.89 506
weighted avg 0.89 0.89 0.89 506Based on validation benchmark, Vision Transformer provides us with the best results.
